Papers by Bei Peng
Evaluating the Evaluation of Diversity in Commonsense Generation (2025.acl-long)
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| Challenge: | Existing evaluation metrics for commonsense generation are unclear on which metrics are best suited for evaluating the diversity of outputs. |
| Approach: | They propose to use a large language model to analyze commonsense generation data to determine which diversity metrics are best suited for commonsensing. |
| Outcome: | The proposed metrics outperform form-based metrics and show high correlations with the LLM-based ratings. |
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks without the need for any fine-tuning. |
| Approach: | They propose a method that diversifies the LLM generations while preserving their quality. |
| Outcome: | The proposed method can be used as training data to improve diversity in existing commonsense generators. |
Synthetic Data Generation for Training Diversified Commonsense Reasoning Models (2026.acl-long)
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| Challenge: | Existing Generative Commonsense Reasoning datasets are created using a small number of human annotators, covering only a narrow set of commonsense scenarios. |
| Approach: | They propose to use a synthetic dataset to train diverse commonsense generators. |
| Outcome: | The proposed model improves both generation diversity and quality compared with vanilla models and human-crafted datasets across different size Large Language Models (LLMs). |